Improvement of AEP Predictions with Time for Swedish Wind Farms

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. Also, there is no apparent increase of error with wind farm size which could have been expected if wind farm blockage effect was a main reason for the overestimations. Besides inaccurate AEP predictions, a higher-than-expected performance decline due to inadequate maintenance of the wind turbines may be a reason behind the AEP overestimations. The main sources of error are insecurity regarding the source of AEP predictions and the omission of mid-life alterations of rated power.

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Evaluation of AEP Predictions for Commercial Wind Farms in Sweden

Applied Sciences ◽

10.3390/app10227995 ◽

2020 ◽

Vol 10 (22) ◽

pp. 7995

Author(s):

Erik Möllerström ◽

Daniel Lindholm

Keyword(s):

South Africa ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Production ◽

Wind Farms ◽

Project Planning ◽

Data Availability ◽

Energy Calculation ◽

The Mean ◽

Over Time

Based on data from 1162 wind turbines, with a rated power of at least 1.8 MW, installed in Sweden after 2005, the accuracy of the annual energy production (AEP) predictions from the project planning phases has been compared to the wind-index-corrected production. Both the production and the predicted AEP data come from the database Vindstat, which collects information directly from wind turbine owners. The mean error was 7.1%, which means that, overall, the predicted AEP has been overestimated. The overestimation was higher for wind turbines situated in open terrain than in forest areas and was higher overall than that previously established for the British Isles and South Africa. Dividing the result over the installation year, the improvement which had been expected due to the continuous refinement of the methods and better data availability, was not observed over time. The major uncertainty comes from the predicted AEP as reported by wind turbine owners to the Vindstat database, which, for some cases, might not come from the wind energy calculation from the planning phase (i.e., the P50-value).

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Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽

10.3390/land10070693 ◽

2021 ◽

Vol 10 (7) ◽

pp. 693

Author(s):

Anna Dóra Sæþórsdóttir ◽

Margrét Wendt ◽

Edita Tverijonaite

Keyword(s):

Land Use ◽

Wind Energy ◽

Wind Turbines ◽

Wind Farm ◽

Service Providers ◽

Wind Farms ◽

Energy Development ◽

Tourism Industry ◽

Land Use Conflicts ◽

Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

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Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

Wind Engineering ◽

10.1177/0309524x21992456 ◽

2021 ◽

pp. 0309524X2199245

Author(s):

Kawtar Lamhour ◽

Abdeslam Tizliouine

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Failure Modes ◽

Wind Farms ◽

Scada System ◽

History Of ◽

Criticality Analysis ◽

Maintenance Model ◽

Reliability And Availability ◽

Critical Subsystems

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

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Effect of Wind Turbine Classes on the Electricity Production of Wind Farms in Cyprus Island

Conference Papers in Energy ◽

10.1155/2013/750958 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Yiannis A. Katsigiannis ◽

George S. Stavrakakis ◽

Christodoulos Pharconides

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Energy Production ◽

Wind Farms ◽

Electricity Production ◽

Wind Speeds ◽

Wind Potential

This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.

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A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽

10.1177/00375497211028605 ◽

2021 ◽

pp. 003754972110286

Author(s):

Eduardo Pérez

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Computational Study ◽

Scheduling Algorithm ◽

Discrete Event ◽

Wind Farms ◽

Online Scheduling ◽

Maintenance Scheduling ◽

Lead Times ◽

System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

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Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽

10.3390/en14144291 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4291

Author(s):

Paxis Marques João Roque ◽

Shyama Pada Chowdhury ◽

Zhongjie Huan

Keyword(s):

Wind Speed ◽

Wind Power ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Wind Farms ◽

Operating Conditions ◽

Wake Effect ◽

Wind Generators ◽

Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

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Synchronization of wind farm power supply into the utility grid

Energy Safety and Energy Economy ◽

10.18635/2071-2219-2021-6-20-25 ◽

2021 ◽

Vol 6 ◽

pp. 20-25

Author(s):

Alexey Bogatyrev

Keyword(s):

Wind Turbines ◽

Power Supply ◽

Distribution System ◽

Wind Farm ◽

Wind Farms ◽

Time Signal ◽

Electricity Distribution ◽

Utility Grid ◽

The Moment ◽

External Time

Wind turbines and wind farms can be connected to the major electricity distribution system. This paper presents the research results on synchronization of wind farm power supply into the utility grid depending on parameters of the grid at the moment. Measurement time gets synchronized with the external time signal delivered from a navigating system like GLONASS. This can help eliminate antiphase operation of individual wind turbines. Connection diagrams and the whole methodology presented in this paper aim to make wind farm power supply into the grid more effective and loss-eliminating.

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Wind farm cooperative control for optimal power generation

Wind Engineering ◽

10.1177/0309524x18780377 ◽

2018 ◽

Vol 42 (6) ◽

pp. 547-560 ◽

Cited By ~ 8

Author(s):

Fa Wang ◽

Mario Garcia-Sanz

Keyword(s):

Power Generation ◽

Wind Turbines ◽

Cooperative Control ◽

Wind Farm ◽

Wind Farms ◽

Practical Method ◽

Individual Case ◽

Power Coefficient ◽

The Individual ◽

Large Wind

The power generation of a wind farm depends on the efficiency of the individual wind turbines of the farm. In large wind farms, wind turbines usually affect each other aerodynamically at some specific wind directions. Previous studies suggest that a way to maximize the power generation of these wind farms is to reduce the generation of the first rows wind turbines to allow the next rows to generate more power (coordinated case). Yet, other studies indicate that the maximum generation of the wind farm is reached when every wind turbine works at its individual maximum power coefficient CPmax (individual case). This article studies this paradigm and proposes a practical method to evaluate when the wind farm needs to be controlled according to the individual or the coordinated case. The discussion is based on basic principles, numerical computations, and wind tunnel experiments.

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Mooring Analysis of a Dual-Spar Floating Wind Farm with a Shared Line

Journal of Offshore Mechanics and Arctic Engineering ◽

10.1115/1.4050965 ◽

2021 ◽

pp. 1-37

Author(s):

Guodong Liang ◽

Zhiyu Jiang ◽

Karl O. Merz

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Wind Farms ◽

Sensitivity Study ◽

Design Stage ◽

Mooring Lines ◽

Natural Period ◽

Preliminary Design Stage ◽

Decay Tests ◽

Line Design

Abstract Wind farms with shared mooring lines have the potential to reduce mooring costs. However, such wind farms may encounter complex system dynamics because adjacent wind turbines are coupled. This paper presents an analysis of the shared mooring system with a focus on the system natural periods. We first apply Irvine's method to model both the shared line and the two-segment single lines. The response surface method is proposed to replace iterations of the catenary equations of the single lines, and a realistic single line design is presented for OC3 Hywind. Then, system linearization and eigenvalue analysis are performed for the wind farm consisting of two spar floating wind turbines, one shared line, and four single lines. The obtained natural periods and natural modes are verified by numerical free decay tests. Finally, a sensitivity study is carried out to investigate the influence of mooring properties. It is found that the shared line has a significant influence on the natural periods in the surge and sway modes. The natural periods in the surge and sway modes are also most sensitive to the mooring property variations. Two sway eigenmodes are identified, and the lower sway natural period varies between 23 s and 88 s in the sensitivity study. The present analysis method can be used to identify critical natural periods at the preliminary design stage of shared mooring systems.

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Managing Wind Power Generation via Indexed Semi-Markov Model and Copula

Energies ◽

10.3390/en13164246 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4246 ◽

Cited By ~ 1

Author(s):

Guglielmo D’Amico ◽

Giovanni Masala ◽

Filippo Petroni ◽

Robert Adam Sobolewski

Keyword(s):

Power Generation ◽

Power Systems ◽

Wind Energy ◽

Output Power ◽

Wind Turbines ◽

Wind Farm ◽

Energy Systems ◽

Wind Farms ◽

Energy System ◽

Spatial Dependencies

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).

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